Abstract
To produce even the simplest human-like behaviors, a humanoid robot must be able to see, act, and react, within a tightly integrated behavioral control system. Although there exists a rich body of literature in Computer Vision, Path Planning, and Feedback Control, wherein many critical subproblems are addressed individually, most demonstrable behaviors for humanoid robots do not effectively integrate elements from all three disciplines. Consequently, tasks that seem trivial to us humans, such as pick-and-place in an unstructured environment, remain far beyond the state-of-the-art in experimental robotics. We view this primarily as a software engineering problem, and have therefore developed MoBeE, a novel behavioral framework for humanoids and other complex robots, which integrates elements from vision, planning, and control, facilitating the synthesis of autonomous, adaptive behaviors. We communicate the efficacy of MoBeE through several demonstrative experiments. We first develop Adaptive Roadmap Planning by integrating a reactive feedback controller into a roadmap planner. Then, an industrial manipulator teaches a humanoid to localize objects as the two robots operate autonomously in a shared workspace. Finally, an integrated vision, planning, control system is applied to a real-world reaching task using the humanoid robot.